What can you do with this server?

This server provides structured access to the GenLayer documentation through three tools: * search_docs: Search the documentation using a natural language query and retrieve the top matching sections, complete with hierarchical title breadcrumbs (e.g., What is GenLayer > Core Technology > On-Chain AI Processing) and full content. * get_section: Fetch the full content of a specific documentation section by providing its exact heading title (e.g., 'Optimistic Democracy' or 'Economic Model'). * list_sections: Get a complete list of all headings and subheadings available in the GenLayer documentation, along with their starting line numbers, useful for navigation and discovery.

How do I use genlayer-docs-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@genlayer-docs-mcp Search GenLayer docs for on-chain AI processing" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

genlayer-docs-mcp

by Laegend14

Overview Schema Source Related Servers Discussions Admin

Python

Local

GenLayer Documentation MCP Server

A standardized, shareable Model Context Protocol (MCP) server that packages and exposes the GenLayer documentation (genlayer-docs.txt) to any AI assistant (including Claude Desktop, Cursor, Windsurf, Gemini, and others).

By packaging this as a Python project with a pyproject.toml and utilizing the official mcp SDK, anyone can run it with a standard Python install — no manual file paths or dependency wrangling.

Available Tools

Once registered, the server exposes the following tools:

search_docs(query: string, top_k: int = 5): Search the GenLayer documentation for relevant sections matching a query. Returns top matching sections along with their hierarchical title breadcrumbs (e.g., What is GenLayer > Core Technology > On-Chain AI Processing) and starting line numbers.
get_section(title: string): Retrieve the full content of a specific documentation section matching the specified heading title.
list_sections(): List all headings and subheadings present in the GenLayer documentation along with their starting line numbers.

Related MCP server: mcp-minecraft-forge

Quick Start (Recommended)

This is the setup verified to work with a standard Python installation on Windows, macOS, and Linux.

1. Install the package

From PyPI:

pip install genlayer-docs-mcp

Or from source (after cloning this repo):

pip install .

2. Register it with your AI client

Add the server inside the mcpServers block of your client's MCP config (Claude Desktop on Windows: %APPDATA%\Claude\claude_desktop_config.json):

{
  "mcpServers": {
    "genlayer-docs": {
      "command": "python",
      "args": [
        "-m",
        "genlayer_docs_mcp"
      ]
    }
  }
}

Why python -m? It uses the Python interpreter already on your PATH, so there is nothing extra to install (unlike uvx, which requires uv to be installed first). Restart your client after saving, and the three tools above will appear.

For Cursor / Windsurf, use the same values in the editor's MCP settings:

Type: command
Command: python
Args: -m genlayer_docs_mcp

Alternative: Zero-install with `uvx`

If you have uv installed, you can run the server without installing it first. Anyone in the world can use this on any AI client with zero local files:

{
  "mcpServers": {
    "genlayer-docs": {
      "command": "uvx",
      "args": ["genlayer-docs-mcp"]
    }
  }
}

Cursor / Windsurf command: uvx genlayer-docs-mcp

Requires uv/uvx on your PATH. Install it with winget install astral-sh.uv (Windows) or see the uv install guide.

Alternative: Install directly from GitHub (no PyPI needed)

To track the latest source, install straight from the public repository:

Using pip:

pip install "git+https://github.com/Laegend14/Genlayer-mcp"

Using uvx (zero-install):

{
  "mcpServers": {
    "genlayer-docs": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/Laegend14/Genlayer-mcp",
        "genlayer-docs-mcp"
      ]
    }
  }
}

Alternative: Global install via `pipx`

Users who prefer an isolated global install can use pipx:

# From PyPI
pipx install genlayer-docs-mcp

# Or from GitHub
pipx install "git+https://github.com/Laegend14/Genlayer-mcp"

Then configure the command in any AI client as:

Command: genlayer-docs-mcp (no arguments required)

Note: this requires the pipx scripts directory to be on your PATH (pipx ensurepath). If your client can't find the genlayer-docs-mcp executable, use the Quick Start python -m method instead.

Development

Clone the repo and install in editable mode:

git clone https://github.com/Laegend14/Genlayer-mcp
cd Genlayer-mcp
pip install -e .

Run the server directly to confirm it loads the documentation:

python -m genlayer_docs_mcp

You should see a log line reporting the number of parsed documentation sections.

Available Tools

3 tools

get_sectionA

Retrieve the full content of a specific documentation section by its exact title (e.g., 'Optimistic Democracy' or 'Economic Model').

ParametersJSON Schema

Name	Required	Description	Default
`title`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.8/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description only states 'Retrieve the full content,' which implies a read operation. It does not disclose any behavioral traits such as idempotency, side effects, access restrictions, or error handling (e.g., if title not found). For a tool with no annotations, the description lacks necessary behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence with an example, front-loaded with the verb 'Retrieve,' no redundant information. Every word serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple single-parameter schema and the presence of an output schema (which presumably documents the return value), the description adequately explains what the tool does. It could mention error cases, but overall it is sufficient for a straightforward retrieval tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaning by specifying that the title must be exact and provides examples. With 0% schema description coverage, this significantly clarifies the parameter's purpose beyond the schema's bare 'string' type. However, it does not specify case-sensitivity or format details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (Retrieve), the resource (full content of a specific documentation section), and the input (exact title) with concrete examples (e.g., 'Optimistic Democracy'). This directly distinguishes it from siblings like list_sections (which lists sections) and search_docs (which searches across docs).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when the exact title is known, but does not explicitly contrast with siblings or provide guidance on when not to use it. The distinction is implicit rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_sectionsA

List all section titles and headings available in the GenLayer documentation.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must fully disclose behavioral traits. It states the action (listing) but does not mention any side effects, authentication needs, rate limits, or output constraints. Minimal behavioral info beyond the obvious.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence with no extraneous words, effectively front-loaded. Every word is informative and earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters and an output schema exists (though not shown), the description is mostly complete. Could potentially mention the output structure but not necessary. Simple tool, adequate context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist, so schema coverage is 100% by default. The description adds no parameter information, which is acceptable as there are no parameters to document. Baseline score of 4 for zero-parameter tools.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'List', the resource 'section titles and headings', and the scope 'in the GenLayer documentation'. It is specific and distinct from siblings like get_section (retrieves a single section) and search_docs (searches across docs).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when needing an overview of available sections but does not explicitly state when to use this tool versus alternatives or provide any exclusions. No guidance on when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_docsA

Search the GenLayer documentation for relevant sections matching a query. Returns the top matching sections with their hierarchical titles and full content.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes
`top_k`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.6/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses that it returns 'top matching sections with their hierarchical titles and full content,' indicating a read-only operation. However, it lacks details on rate limits, authorization, or constraints like maximum top_k value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core purpose, no unnecessary words. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and low schema coverage, the description is somewhat adequate but misses details on parameter semantics and lacks full behavioral disclosure (e.g., pagination, error handling).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% coverage, yet the description does not explain the 'top_k' parameter (integer, default 5) and only implicitly covers 'query.' This leaves agents without guidance on how to use the optional parameter effectively.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Search the GenLayer documentation for relevant sections matching a query.' It distinguishes from siblings like get_section and list_sections by focusing on searching based on a query.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for finding documentation sections, but does not explicitly state when not to use it or provide alternatives. Sibling tools are listed but not referenced in guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

3 tool updatesv0.1.0Jul 8, 2026
- First observedget_section
- First observedlist_sections
- First observedsearch_docs

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4/5.0

Scored 2026-07-08 21:25 across 3 tools

Disambiguation5/5

Each tool has a distinct purpose: listing all sections, retrieving a specific section by exact title, and searching for relevant sections. No overlap.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (get_section, list_sections, search_docs), making them predictable.

Tool Count5/5

Three tools are appropriate for a documentation server, covering the essential operations without unnecessary complexity.

Completeness4/5

Covers listing, retrieval, and search. A minor gap might be lacking a 'get all sections with content' operation, but the current set is sufficient for most use cases.

Maintenance

ActivityStale

ResponsivenessNo issues

Resources

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GenLayer Documentation MCP Server

Available Tools

Quick Start (Recommended)

1. Install the package

2. Register it with your AI client

Alternative: Zero-install with uvx

Alternative: Install directly from GitHub (no PyPI needed)

Alternative: Global install via pipx

Development

Available Tools

Output Schema

Output Schema

Output Schema

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Alternative: Zero-install with `uvx`

Alternative: Global install via `pipx`